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dc.contributor.author | De Oña, Juan | es_ES |
dc.contributor.author | López-Maldonado, Griselda | es_ES |
dc.contributor.author | Mujalli, Randa | es_ES |
dc.contributor.author | Calvo, Francisco J. | es_ES |
dc.date.accessioned | 2018-07-07T04:23:50Z | |
dc.date.available | 2018-07-07T04:23:50Z | |
dc.date.issued | 2013 | es_ES |
dc.identifier.issn | 0001-4575 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/105464 | |
dc.description.abstract | [EN] One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3,229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BN) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analyzed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Accident Analysis & Prevention | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Cluster Analysis | es_ES |
dc.subject | Latent Class Clustering | es_ES |
dc.subject | Bayesian Networks | es_ES |
dc.subject | Traffic accidents | es_ES |
dc.subject | Classification | es_ES |
dc.subject | Injury severity | es_ES |
dc.subject | Highways | es_ES |
dc.subject | Road safety | es_ES |
dc.subject.classification | INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES | es_ES |
dc.title | Analysis of traffic accidents on rural highways using Latent Class Clustering | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.aap.2012.10.016 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería e Infraestructura de los Transportes - Departament d'Enginyeria i Infraestructura dels Transports | es_ES |
dc.description.bibliographicCitation | De Oña, J.; López-Maldonado, G.; Mujalli, R.; Calvo, FJ. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering. Accident Analysis & Prevention. 51:1-10. doi:10.1016/j.aap.2012.10.016 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.aap.2012.10.016 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 10 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 51 | es_ES |
dc.relation.pasarela | S\350482 | es_ES |